Abstract
Hydrostatic systems are considered as essential supporting structures in heavy machine tools. The calculations and analyses for hydrostatic bearings are always laborious because of the involvement of several disciplines such as elastic mechanics, hydromechanics, thermodynamics, and other factors in the design. It is well known that large data and cloud technology are capable of processing and transmission of hydrostatic system studies. In this work, a cloud manufacture model is presented to provide big data storage, transmission, and processing platform for designers, manufacturers, and users of hydrostatic bearings. All participants involved in the manufacturing were linked together by timely information communication in order to ensure that the customized products met with the actual conditions in the cloud server. Based on the actual requirements of the user, the carrying capacity of hydrostatic system was analyzed by a designer using finite difference method and the results were sent to the manufacturer for machining of components. The monitored data from the consumer could be fed back to the designer for performance evaluation through the cloud server. It was found that customized manufacturing tasks could be finished more reliably and efficiently if all participants exchanged the data through cloud platform in real time.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Zhang L, Luo Y, Tao F, Bo H, Li LR, Zhang X, Guo H, Cheng Y, Anrui H, Liu Y (2012) Cloud manufacturing: a new manufacturing paradigm. Enterp Inf Syst. doi:10.1080/17517575.2012.683812
Tao F, Zhang L, Liu YK, Cheng Y, Wang LH, Xun X (2015) Manufacturing service management in cloud manufacturing: overview and future research directions. J Manuf Sci Eng Trans ASME. doi:10.1115/1.4030510
Sharifi H, Colquhoun G, Barclay I, Dann Z (2001) Agile manufacturing: a management and operational framework. Proc Inst Mech Eng B J Eng Manuf 215(6):857–869
Meier H, Roy R, Seliger G (2010) Industrial product-service systems—IPS2. CIRP Ann Mfg Technol 59(2):607–627
Tao F, Zhang L, Liu Y, Cheng Y, Wang L, Xu X (2015) Manufacturing service management in cloud manufacturing: overview and future research directions. J Manuf Sci Eng 137:040912
Tao F, Zhang L, Venkatesh VC, Luo Y, Cheng Y (2011) Cloud manufacturing: a computing and service oriented manufacturing model. Proc IMechE Part B J Eng Manuf 225:1969–1976
Tao F, Cheng Y, Zhang L, Nee AYC (2015) Advanced manufacturing systems: socialization characteristics and trends. J Intell Manuf. doi:10.1007/s10845-015-1042-8
Tao F, Cheng Y, Xu LD, Lin Z, Li BH (2014) CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system. IEEE Trans Ind Inform 10(2):1435–1442
Li J, Tao F, Cheng Y, Zhao L (2015) Big data in product lifecycle management. Int J Adv Manuf Technol 81:667–684
Luo Y, Zhang L, Tao F, Ren L, Liu YK, Zhang ZQ (2013) A modeling and description method of multidimensional information for manufacturing capability in cloud manufacturing system. Int J Adv Manuf Technol 69:961–975
Tao F, Hu Y, Zhao D, Zhou Z (2009) Study on resource service match and search in manufacturing grid system. Int J Adv Manuf Technol 43:379–399. doi:10.1007/s00170-008-1699-7
Tao F, Hu Y, Zhou Z (2007) Study on manufacturing grid and its resource service optimal-selection system. Int J Adv Manuf Technol. doi:10.1007/s00170-007-1033-9
Wang L, Wang W, Wang H, Ma T, Hu Y (2014) Numerical analysis on the factors affecting the hydrodynamic performance for the parallel surfaces with microtextures. J Tribol 136(2):021702
Meng X, Bai S, Peng X (2014) Lubrication film flow control by oriented dimples for liquid lubricated mechanical seals. Tribol Int 77:132–141
Tao F, Zuo Y, Xu LD, Zhang L (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Ind Inform 10(2):1547–1557
Howe J (2009) Why the power of the crowd is driving the future of business, 1st edn. China CITIC Press, Beijing
Qiu MF, Bailey BN, Raeymaekers RSB (2014) The accuracy of the compressible Reynolds equation for predicting the local pressure in gas-lubricated textured parallel slider bearings. Tribol Int 72:83–89
Ochoa ED, Otero JE, Lopez AS, Tanarro EC (2015) Film thickness predictions for line contact using a new Reynolds–Carreau equation. Tribol Int 82:133–141
Habchi W, Bair S, Qureshi F, Covitch M (2013) A film thickness correction formula for double-newtonian shear-thinning in rolling EHL circular contacts. Tribol Lett 50:59–66
Bair SA (2006) Reynolds–Ellis equation for line contact with shear-thinning. Tribol Int 39(4):310–316
Khlifi ME (2007) Numerical modeling of non-newtonian fluids in slider bearings and channel thermohydrodynamic flow. J Tribol 129:695–699
Cheng Q, Zhao HW, Zhang GJ, Gu PH, Cai LG (2014) An analytical approach for crucial geometric errors identification of multi-axis machine tool based on global sensitivity analysis. Int J Adv Manuf Technol 75(10):107–121
Cheng Q, Feng QN, Liu ZF, Gu PH, Cai LG (2014) Fluctuation prediction of machining accuracy for multi-axis machine tool based on stochastic process theory. Proc Inst Mech Eng C J Mech Eng Sci. doi:10.1177/0954406214562633
Ji JH, Fu YH, Bi QS (2014) Influence of geometric shapes on the hydrodynamic lubrication of a partially textured slider with micro-grooves. J Tribol 136:041702-1-8
Masjedi M, Khonsari MM (2015) On the effect of surface roughness in point-contact EHL: formulas for film thickness and asperity load. Tribol Int 82:228–244
Leem CS, Lee HJ (2004) Development of certification and audit processes of application service provider for IT outsourcing. Technovation 24(1):63–71
Rosenthal M, Mork P, Li MH, Stanford J, Koester D, Reynolds P (2009) Cloud computing: a new business paradigm for biomedical information sharing. J Biomed Inform 43(2):342–353
Li YZ, Zhou K, Zhang Z (2015) A flow-difference feedback iteration method and its application to high-speed aerostatic journal bearings. Tribol Int 84:132–141
Nicoletti R (2013) Comparison between a meshless method and the finite difference method for solving the Reynolds equation in finite bearings. J Tribol 135(10):044501-1-9
Li J, Chen HS (2007) Evaluation on applicability of Reynolds equation for squared transverse roughness compared to CFD. J Tribol 129(10):963–967
Getachew AD, Prawal S (2011) THD analysis for finite slider bearing with roughness: special reference to load generation in parallel sliders. Acta Mech. doi:10.1007/s00707-011-0515-x
Tao F, LaiLi Y, Xu L, Zhang L (2013) FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans Ind Inform 9(4):2023–2033
Tao F, Zhao D, Hu Y, Zhou Z (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Trans Ind Inform 4(4):215–237
Wang L (2014) Machine availability monitoring and machining process planning towards cloud manufacturing. CIRP J Manuf Sci Technol 6:263–273
de Rafaelli de CC, Lúcia MAD, Yuri F, de Daniel O (2015) Optimizing virtual machine allocation for parallel scientific workflows in federated clouds. Futur Gener Comput Syst 46:51–68
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, Z., Wang, Y., Cai, L. et al. Design and manufacturing model of customized hydrostatic bearing system based on cloud and big data technology. Int J Adv Manuf Technol 84, 261–273 (2016). https://doi.org/10.1007/s00170-015-8066-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-015-8066-2